fix hswishquant and hsigmoidquant validation false bug

mindspore/nn/layer/quant.py

@@ -920,7 +920,7 @@ class HSwishQuant(_QuantActivation):
                                                         symmetric=symmetric,
                                                         narrow_range=narrow_range,
                                                         quant_delay=quant_delay)
-        if isinstance(activation, nn.HSwish):
+        if issubclass(activation, nn.HSwish):
             self.act = activation()
         else:
             raise ValueError("Activation should be `nn.HSwish`")
@@ -989,7 +989,7 @@ class HSigmoidQuant(_QuantActivation):
                                                         symmetric=symmetric,
                                                         narrow_range=narrow_range,
                                                         quant_delay=quant_delay)
-        if isinstance(activation, nn.HSwish):
+        if issubclass(activation, nn.HSwish):
             self.act = activation()
         else:
             raise ValueError("Activation should be `nn.HSigmoid`")

model_zoo/mobilenetv2/train.py

@@ -18,6 +18,7 @@ import time
 import argparse
 import random
 import numpy as np
+
 from mindspore import context
 from mindspore import Tensor
 from mindspore import nn
@@ -32,8 +33,9 @@ from mindspore.train.model import Model, ParallelMode
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.communication.management import init, get_group_size
+from mindspore.communication.management import init, get_group_size, get_rank
 import mindspore.dataset.engine as de
+
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
 from src.config import config_gpu, config_ascend
@@ -60,9 +62,14 @@ if args_opt.platform == "Ascend":
                         device_id=device_id, save_graphs=False)
 elif args_opt.platform == "GPU":
     context.set_context(mode=context.GRAPH_MODE,
-                        device_target="GPU", save_graphs=False)
+                        device_target="GPU",
+                        save_graphs=False)
+    init("nccl")
+    context.set_auto_parallel_context(device_num=get_group_size(),
+                                      parallel_mode=ParallelMode.DATA_PARALLEL,
+                                      mirror_mean=True)
 else:
-    raise ValueError("Unsupport platform.")
+    raise ValueError("Unsupported device target.")
 
 
 class CrossEntropyWithLabelSmooth(_Loss):
@@ -155,12 +162,8 @@ class Monitor(Callback):
 if __name__ == '__main__':
     if args_opt.platform == "GPU":
         # train on gpu
-        print("train args: ", args_opt, "\ncfg: ", config_gpu)
-
-        init('nccl')
-        context.set_auto_parallel_context(parallel_mode="data_parallel",
-                                          mirror_mean=True,
-                                          device_num=get_group_size())
+        print("train args: ", args_opt)
+        print("cfg: ", config_gpu)
 
         # define net
         net = mobilenet_v2(num_classes=config_gpu.num_classes, platform="GPU")
@@ -201,13 +204,13 @@ if __name__ == '__main__':
                       loss_scale_manager=loss_scale)
 
         cb = [Monitor(lr_init=lr.asnumpy())]
+        ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
         if config_gpu.save_checkpoint:
             config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
                                          keep_checkpoint_max=config_gpu.keep_checkpoint_max)
-            ckpt_cb = ModelCheckpoint(
-                prefix="mobilenetV2", directory=config_gpu.save_checkpoint_path, config=config_ck)
+            ckpt_cb = ModelCheckpoint(prefix="mobilenetV2", directory=ckpt_save_dir, config=config_ck)
             cb += [ckpt_cb]
-        # begine train
+        # begin train
         model.train(epoch_size, dataset, callbacks=cb)
     elif args_opt.platform == "Ascend":
         # train on ascend

model_zoo/mobilenetv3/train.py

@@ -18,6 +18,7 @@ import time
 import argparse
 import random
 import numpy as np
+
 from mindspore import context
 from mindspore import Tensor
 from mindspore import nn
@@ -33,7 +34,8 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 import mindspore.dataset.engine as de
-from mindspore.communication.management import init, get_group_size
+from mindspore.communication.management import init, get_group_size, get_rank
+
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
 from src.config import config_gpu, config_ascend
@@ -57,10 +59,16 @@ if args_opt.platform == "Ascend":
     device_id = int(os.getenv('DEVICE_ID'))
     context.set_context(mode=context.GRAPH_MODE,
                         device_target="Ascend",
-                        device_id=device_id, save_graphs=False)
+                        device_id=device_id,
+                        save_graphs=False)
 elif args_opt.platform == "GPU":
     context.set_context(mode=context.GRAPH_MODE,
-                        device_target="GPU", save_graphs=False)
+                        device_target="GPU",
+                        save_graphs=False)
+    init("nccl")
+    context.set_auto_parallel_context(device_num=get_group_size(),
+                                      parallel_mode=ParallelMode.DATA_PARALLEL,
+                                      mirror_mean=True)
 else:
     raise ValueError("Unsupport platform.")
 
@@ -155,12 +163,8 @@ class Monitor(Callback):
 if __name__ == '__main__':
     if args_opt.platform == "GPU":
         # train on gpu
-        print("train args: ", args_opt, "\ncfg: ", config_gpu)
-
-        init('nccl')
-        context.set_auto_parallel_context(parallel_mode="data_parallel",
-                                          mirror_mean=True,
-                                          device_num=get_group_size())
+        print("train args: ", args_opt)
+        print("cfg: ", config_gpu)
 
         # define net
         net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
@@ -201,11 +205,11 @@ if __name__ == '__main__':
                       loss_scale_manager=loss_scale)
 
         cb = [Monitor(lr_init=lr.asnumpy())]
+        ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
         if config_gpu.save_checkpoint:
             config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
                                          keep_checkpoint_max=config_gpu.keep_checkpoint_max)
-            ckpt_cb = ModelCheckpoint(
-                prefix="mobilenetV3", directory=config_gpu.save_checkpoint_path, config=config_ck)
+            ckpt_cb = ModelCheckpoint(prefix="mobilenetV3", directory=ckpt_save_dir, config=config_ck)
             cb += [ckpt_cb]
         # begine train
         model.train(epoch_size, dataset, callbacks=cb)